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Depth Estimation Using an Infrared Dot Projector and an Infrared Color Stereo Camera

机译:使用红外点投影仪和红外彩色立体相机的深度估计

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This paper proposes a method of estimating depth from two kinds of stereo images: color stereo images and infrared stereo images. An infrared dot pattern is projected on a scene by a projector so that infrared cameras can capture the scene textured by the dots and the depth can be estimated even where the surface is not textured. The cost volumes are calculated for the infrared and color stereo images for each frame and are extended in the time direction to define a spatiotemporal cost volume (st-cost volume). We also extend the cost volume filter in the time direction by modifying the cross-based local multipoint filter (CLMF) and applying it to the st-cost volumes in order to restrain flicker on the time-varying depth maps. To get a reliable cost volume, the infrared and color st-cost volumes are integrated into a single cost volume by selecting the cost of either the infrared or the color st-cost volumes according to the size of the adaptive kernel used for the CLMF. Then, a graphcut is executed on the cost volume in order to estimate the disparity robustly even when the baselines of the stereo cameras are set wide enough to ensure spatially high resolution in the depth direction and the shapes of blocks are deformed by the affine transformation. A 2D graphcut is executed on each scan line to reduce the processing time and memory consumption. We experimented with the proposed method using infrared color stereo data sets of scenes in the real world and evaluated its effectiveness by comparing it with other recent stereo matching methods and depth cameras.
机译:本文提出了一种从两种立体图像中估计深度的方法:彩色立体图像和红外立体图像。投影机将红外点图案投影到场景上,以便红外摄像机可以捕获由点纹理化的场景,并且即使表面没有纹理,也可以估算深度。为每帧红外和彩色立体图像计算成本量,并在时间方向上扩展以定义时空成本量(st-cost volume)。我们还通过修改基于交叉的局部多点滤波器(CLMF)并将其应用于st-cost量,在时间方向上扩展了cost-volume过滤器,以抑制随时间变化的深度图上的闪烁。为了获得可靠的成本量,通过根据用于CLMF的自适应内核的大小选择红外或色度成本量的成本,将红外和色度成本量集成为一个成本量。然后,即使在将立体摄像机的基线设置得足够宽以确保深度方向上的空间高分辨率以及通过仿射变换使块的形状变形的情况下,也要对成本量执行切图以可靠地估计视差。在每条扫描线上执行2D图形切割,以减少处理时间和内存消耗。我们使用现实世界中场景的红外彩色立体数据集对提出的方法进行了实验,并通过将其与其他最新的立体匹配方法和深度相机进行比较来评估了其有效性。

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